Frontiers in Oncology (Nov 2022)

The transcriptomic landscape of elderly acute myeloid leukemia identifies B7H3 and BANP as a favorable signature in high-risk patients

  • Sara Villar,
  • Sara Villar,
  • Beñat Ariceta,
  • Beñat Ariceta,
  • Beñat Ariceta,
  • Xabier Agirre,
  • Xabier Agirre,
  • Aura Daniela Urribarri,
  • Rosa Ayala,
  • David Martínez-Cuadrón,
  • Juan Miguel Bergua,
  • Susana Vives,
  • Lorenzo Algarra,
  • Mar Tormo,
  • Pilar Martínez,
  • Josefina Serrano,
  • Catia Simoes,
  • Pilar Herrera,
  • Maria José Calasanz,
  • Maria José Calasanz,
  • Ana Alfonso-Piérola,
  • Ana Alfonso-Piérola,
  • Bruno Paiva,
  • Bruno Paiva,
  • Bruno Paiva,
  • Joaquín Martínez-López,
  • Jesús F. San Miguel,
  • Jesús F. San Miguel,
  • Felipe Prósper,
  • Felipe Prósper,
  • Pau Montesinos

DOI
https://doi.org/10.3389/fonc.2022.1054458
Journal volume & issue
Vol. 12

Abstract

Read online

Acute myeloid leukemia (AML) in the elderly remains a clinical challenge, with a five-year overall survival rate below 10%. The current ELN 2017 genetic risk classification considers cytogenetic and mutational characteristics to stratify fit AML patients into different prognostic groups. However, this classification is not validated for elderly patients treated with a non-intensive approach, and its performance may be suboptimal in this context. Indeed, the transcriptomic landscape of AML in the elderly has been less explored and it might help stratify this group of patients. In the current study, we analyzed the transcriptome of 224 AML patients > 65 years-old at diagnosis treated in the Spanish PETHEMA-FLUGAZA clinical trial in order to identify new prognostic biomarkers in this population. We identified a specific transcriptomic signature for high-risk patients with mutated TP53 or complex karyotype, revealing that low expression of B7H3 gene with high expression of BANP gene identifies a subset of high-risk AML patients surviving more than 12 months. This result was further validated in the BEAT AML cohort. This unique signature highlights the potential of transcriptomics to identify prognostic biomarkers in in elderly AML.

Keywords